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Non Hybrid Long Read Consensus Using Local De Bruijn Graph Assembly

German Tischler, Eugene W. Myers
doi: https://doi.org/10.1101/106252
German Tischler
Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstraße 108, 01307 Dresden, Germany
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  • For correspondence: tischler@mpi-cbg.de
Eugene W. Myers
Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstraße 108, 01307 Dresden, Germany
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Abstract

While second generation sequencing led to a vast increase in sequenced data, the shorter reads which came with it made assembly a much harder task and for some regions impossible with only short read data. This changed again with the advent of third generation long read sequencers. The length of the long reads allows a much better resolution of repetitive regions, their high error rate however is a major challenge. Using the data successfully requires to remove most of the sequencing errors. The first hybrid correction methods used low noise second generation data to correct third generation data, but this approach has issues when it is unclear where to place the short reads due to repeats and also because second generation sequencers fail to sequence some regions which third generation sequencers work on. Later non hybrid methods appeared. We present a new method for non hybrid long read error correction based on De Bruijn graph assembly of short windows of long reads with subsequent combination of these correct windows to corrected long reads. Our experiments show that this method yields a better correction than other state of the art non hybrid correction approaches.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 06, 2017.
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Non Hybrid Long Read Consensus Using Local De Bruijn Graph Assembly
German Tischler, Eugene W. Myers
bioRxiv 106252; doi: https://doi.org/10.1101/106252
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Non Hybrid Long Read Consensus Using Local De Bruijn Graph Assembly
German Tischler, Eugene W. Myers
bioRxiv 106252; doi: https://doi.org/10.1101/106252

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